Overview

Dataset statistics

Number of variables13
Number of observations2968
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.6 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with q_invoices and 4 other fieldsHigh correlation
recency_days is highly correlated with q_invoicesHigh correlation
q_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
q_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
q_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with q_returns and 1 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with avg_basket_sizeHigh correlation
q_returns is highly correlated with gross_revenue and 5 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 25.15706781) Skewed
frequency is highly skewed (γ1 = 24.87675009) Skewed
q_returns is highly skewed (γ1 = 21.9754032) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 33 (1.1%) zeros Zeros
q_returns has 1481 (49.9%) zeros Zeros

Reproduction

Analysis started2022-11-12 21:23:55.351230
Analysis finished2022-11-12 21:24:16.634723
Duration21.28 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2316.666442
Minimum0
Maximum5714
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-12T18:24:16.778386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.35
Q1928.5
median2119.5
Q33536.25
95-th percentile5034.3
Maximum5714
Range5714
Interquartile range (IQR)2607.75

Descriptive statistics

Standard deviation1554.722712
Coefficient of variation (CV)0.6711033938
Kurtosis-1.010637904
Mean2316.666442
Median Absolute Deviation (MAD)1270.5
Skewness0.3426249769
Sum6875866
Variance2417162.71
MonotonicityStrictly increasing
2022-11-12T18:24:16.940657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
30101
 
< 0.1%
29951
 
< 0.1%
29961
 
< 0.1%
29991
 
< 0.1%
30001
 
< 0.1%
30011
 
< 0.1%
30021
 
< 0.1%
30051
 
< 0.1%
30071
 
< 0.1%
Other values (2958)2958
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57141
< 0.1%
56951
< 0.1%
56851
< 0.1%
56791
< 0.1%
56581
< 0.1%
56541
< 0.1%
56481
< 0.1%
56371
< 0.1%
56361
< 0.1%
56261
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.37702
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-12T18:24:17.324107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.35
Q113798.75
median15220.5
Q316768.5
95-th percentile17964.65
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.144523
Coefficient of variation (CV)0.1125803587
Kurtosis-1.206178196
Mean15270.37702
Median Absolute Deviation (MAD)1489
Skewness0.03219371129
Sum45322479
Variance2955457.892
MonotonicityNot monotonic
2022-11-12T18:24:17.467958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
126701
 
< 0.1%
177341
 
< 0.1%
149051
 
< 0.1%
161031
 
< 0.1%
146261
 
< 0.1%
148681
 
< 0.1%
182461
 
< 0.1%
171151
 
< 0.1%
166111
 
< 0.1%
Other values (2958)2958
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2953
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2693.389373
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-12T18:24:17.587305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.7325
Q1570.845
median1085.51
Q32306.905
95-th percentile7169.562
Maximum279138.02
Range279131.82
Interquartile range (IQR)1736.06

Descriptive statistics

Standard deviation10135.32607
Coefficient of variation (CV)3.763037818
Kurtosis397.3184084
Mean2693.389373
Median Absolute Deviation (MAD)671.39
Skewness17.63574461
Sum7993979.66
Variance102724834.5
MonotonicityNot monotonic
2022-11-12T18:24:17.695970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1078.962
 
0.1%
2053.022
 
0.1%
3312
 
0.1%
1353.742
 
0.1%
889.932
 
0.1%
745.062
 
0.1%
379.652
 
0.1%
2092.322
 
0.1%
731.92
 
0.1%
734.942
 
0.1%
Other values (2943)2948
99.3%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140438.721
< 0.1%
124564.531
< 0.1%
117375.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%
65019.621
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.31030997
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-12T18:24:17.841221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.76031378
Coefficient of variation (CV)1.209142264
Kurtosis2.77659321
Mean64.31030997
Median Absolute Deviation (MAD)26
Skewness1.79807024
Sum190873
Variance6046.666399
MonotonicityNot monotonic
2022-11-12T18:24:18.022695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
285
 
2.9%
385
 
2.9%
876
 
2.6%
1067
 
2.3%
966
 
2.2%
766
 
2.2%
1764
 
2.2%
2255
 
1.9%
Other values (262)2218
74.7%
ValueCountFrequency (%)
033
 
1.1%
199
3.3%
285
2.9%
385
2.9%
487
2.9%
543
1.4%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3724
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

q_invoices
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.724056604
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-12T18:24:18.171547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.857882575
Coefficient of variation (CV)1.5474834
Kurtosis190.7771511
Mean5.724056604
Median Absolute Deviation (MAD)2
Skewness10.76520644
Sum16989
Variance78.46208371
MonotonicityNot monotonic
2022-11-12T18:24:18.290115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2785
26.4%
3498
16.8%
4393
13.2%
5237
 
8.0%
1190
 
6.4%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
Other values (46)332
11.2%
ValueCountFrequency (%)
1190
 
6.4%
2785
26.4%
3498
16.8%
4393
13.2%
5237
 
8.0%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

q_items
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1664
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1579.712264
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-12T18:24:18.410065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101.35
Q1296
median638
Q31398.25
95-th percentile4403.25
Maximum196844
Range196843
Interquartile range (IQR)1102.25

Descriptive statistics

Standard deviation5700.529956
Coefficient of variation (CV)3.608587516
Kurtosis518.1228414
Mean1579.712264
Median Absolute Deviation (MAD)419
Skewness18.7602581
Sum4688586
Variance32496041.78
MonotonicityNot monotonic
2022-11-12T18:24:18.540095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
889
 
0.3%
1509
 
0.3%
2608
 
0.3%
848
 
0.3%
2888
 
0.3%
2728
 
0.3%
2468
 
0.3%
5167
 
0.2%
3947
 
0.2%
Other values (1654)2885
97.2%
ValueCountFrequency (%)
11
< 0.1%
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
799631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
628121
< 0.1%
582431
< 0.1%
577851
< 0.1%
502551
< 0.1%

q_products
Real number (ℝ≥0)

HIGH CORRELATION

Distinct469
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.7456199
Minimum1
Maximum7837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-12T18:24:18.661993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7837
Range7836
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.8785162
Coefficient of variation (CV)2.198681439
Kurtosis354.7550751
Mean122.7456199
Median Absolute Deviation (MAD)44
Skewness15.70464041
Sum364309
Variance72834.41353
MonotonicityNot monotonic
2022-11-12T18:24:18.787480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2845
 
1.5%
2038
 
1.3%
3535
 
1.2%
1533
 
1.1%
2933
 
1.1%
1933
 
1.1%
1132
 
1.1%
2631
 
1.0%
2730
 
1.0%
2529
 
1.0%
Other values (459)2629
88.6%
ValueCountFrequency (%)
16
 
0.2%
214
0.5%
315
0.5%
417
0.6%
526
0.9%
629
1.0%
718
0.6%
819
0.6%
927
0.9%
1027
0.9%
ValueCountFrequency (%)
78371
< 0.1%
56701
< 0.1%
50951
< 0.1%
45771
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16361
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct2965
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.99655282
Minimum2.150588235
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-12T18:24:18.920429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.915887985
Q113.11811111
median17.96548505
Q324.98179365
95-th percentile90.052125
Maximum4453.43
Range4451.279412
Interquartile range (IQR)11.86368254

Descriptive statistics

Standard deviation119.5318165
Coefficient of variation (CV)3.622554671
Kurtosis812.969606
Mean32.99655282
Median Absolute Deviation (MAD)5.980669355
Skewness25.15706781
Sum97933.76878
Variance14287.85517
MonotonicityNot monotonic
2022-11-12T18:24:19.042932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152
 
0.1%
4.1622
 
0.1%
14.478333332
 
0.1%
18.152222221
 
< 0.1%
13.927368421
 
< 0.1%
36.244117651
 
< 0.1%
29.784166671
 
< 0.1%
22.87926231
 
< 0.1%
20.511041671
 
< 0.1%
149.0251
 
< 0.1%
Other values (2955)2955
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%
615.751
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.30505288
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-12T18:24:19.170974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.9271978
median48.26785714
Q385.33333333
95-th percentile200.65
Maximum366
Range365
Interquartile range (IQR)59.40613553

Descriptive statistics

Standard deviation63.50325927
Coefficient of variation (CV)0.9435139941
Kurtosis4.908645262
Mean67.30505288
Median Absolute Deviation (MAD)26.26785714
Skewness2.06622239
Sum199761.397
Variance4032.663938
MonotonicityNot monotonic
2022-11-12T18:24:19.297487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
422
 
0.7%
7021
 
0.7%
720
 
0.7%
3519
 
0.6%
4918
 
0.6%
1117
 
0.6%
4617
 
0.6%
2117
 
0.6%
2816
 
0.5%
Other values (1248)2776
93.5%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
422
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1138262908
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-12T18:24:19.428602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008893504781
Q10.01633986928
median0.02589835169
Q30.04942659085
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.03308672157

Descriptive statistics

Standard deviation0.4082214549
Coefficient of variation (CV)3.586354717
Kurtosis989.0590635
Mean0.1138262908
Median Absolute Deviation (MAD)0.0121968864
Skewness24.87675009
Sum337.8364311
Variance0.1666447562
MonotonicityNot monotonic
2022-11-12T18:24:19.550821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1198
 
6.7%
0.0277777777817
 
0.6%
0.062517
 
0.6%
0.0238095238116
 
0.5%
0.0909090909115
 
0.5%
0.0833333333315
 
0.5%
0.0344827586214
 
0.5%
0.0294117647114
 
0.5%
0.0357142857113
 
0.4%
0.0769230769213
 
0.4%
Other values (1215)2636
88.8%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
26
 
0.2%
1.1428571431
 
< 0.1%
1198
6.7%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%
0.53
 
0.1%

q_returns
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct213
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.88847709
Minimum0
Maximum9014
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-12T18:24:19.684126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation282.864784
Coefficient of variation (CV)8.107685048
Kurtosis596.2019916
Mean34.88847709
Median Absolute Deviation (MAD)1
Skewness21.9754032
Sum103549
Variance80012.48604
MonotonicityNot monotonic
2022-11-12T18:24:19.808363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
743
 
1.4%
843
 
1.4%
Other values (203)705
23.8%
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
561
 
2.1%
678
 
2.6%
743
 
1.4%
843
 
1.4%
941
 
1.4%
ValueCountFrequency (%)
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%
15941
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1972
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean235.7885065
Minimum1
Maximum6009.333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-12T18:24:19.931622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.2375
median172
Q3281.375
95-th percentile598.345
Maximum6009.333333
Range6008.333333
Interquartile range (IQR)178.1375

Descriptive statistics

Standard deviation283.7237528
Coefficient of variation (CV)1.203297637
Kurtosis103.0742725
Mean235.7885065
Median Absolute Deviation (MAD)82.625
Skewness7.717538936
Sum699820.2873
Variance80499.16789
MonotonicityNot monotonic
2022-11-12T18:24:20.056745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
739
 
0.3%
869
 
0.3%
829
 
0.3%
888
 
0.3%
758
 
0.3%
608
 
0.3%
1368
 
0.3%
1307
 
0.2%
Other values (1962)2881
97.1%
ValueCountFrequency (%)
12
0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%
2082.2258061
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct910
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.49039145
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-12T18:24:20.180897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.666666667
median13.6
Q322.03571429
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.36904762

Descriptive statistics

Standard deviation15.4620774
Coefficient of variation (CV)0.8840326672
Kurtosis29.30304319
Mean17.49039145
Median Absolute Deviation (MAD)6.6
Skewness3.434441407
Sum51911.48183
Variance239.0758377
MonotonicityNot monotonic
2022-11-12T18:24:20.305606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1343
 
1.4%
942
 
1.4%
1641
 
1.4%
839
 
1.3%
1737
 
1.2%
1437
 
1.2%
736
 
1.2%
1136
 
1.2%
534
 
1.1%
1534
 
1.1%
Other values (900)2589
87.2%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333336
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.4%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
2591
< 0.1%
1771
< 0.1%
1481
< 0.1%
1271
< 0.1%
1051
< 0.1%
1041
< 0.1%
1011
< 0.1%
981
< 0.1%
95.51
< 0.1%
94.333333331
< 0.1%

Interactions

2022-11-12T18:24:14.819538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:23:58.601657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:23:59.959965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:01.354269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:02.739083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:04.155682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:05.309878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:06.862231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:08.037485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:09.389630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:10.955937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:12.229353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:13.485993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:14.920506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:23:58.801426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:00.060357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:01.446297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:02.861799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:04.252938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:05.394765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:06.947961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:08.121912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:09.508137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:11.050446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:12.325199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:13.575140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:15.020446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:23:58.923375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:00.156295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:01.541412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:02.956682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:04.331575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:05.517889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:07.034634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:08.207569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:09.606375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:11.148406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:12.420814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:13.668117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:15.129129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:23:59.022898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:00.247652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:01.657932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:03.042498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:04.412235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:05.623399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:07.124869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:08.293035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:09.735285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:11.240993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:12.537021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:13.769682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:15.249648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:23:59.111080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:00.375834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:01.753792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:03.134536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:04.495074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:05.770546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:07.219369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:08.382225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:09.871250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:11.338037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:12.637099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:13.874400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:15.343218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:23:59.210369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:00.496670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:01.992206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:03.239578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:04.570757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:06.081364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:07.305141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:08.462398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:09.970509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:11.427926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:12.721099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:13.972200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:15.463682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:23:59.326112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:00.625692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:02.098658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:03.334399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:04.658081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:06.201120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:07.404210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:08.573778image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:10.083127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:11.538166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:12.813890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:14.080797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:15.569350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:23:59.428456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:00.778040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:02.186213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:03.434025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:04.747863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:06.310711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:07.498520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:08.672714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:10.184972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:11.638457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:12.913994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:14.194638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:15.663149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:23:59.506999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:00.893357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:02.267407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:03.567448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:04.826782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:06.398256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:07.583775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:08.776328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:10.281960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:11.728473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:13.006380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:14.290734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:15.768758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:23:59.592215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:01.005094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:02.353129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:03.707374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:04.914351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:06.496838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:07.675558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:08.898269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:10.386197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:11.826211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:13.100795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:14.404007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:15.876899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:23:59.678092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:01.098093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:02.440935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:03.832897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:05.021396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:06.598918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:07.769295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:09.013887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:10.483546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:11.931241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:13.199726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:14.507314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:15.974700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:23:59.765988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:01.179085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:02.537180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:03.924329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:05.130403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:06.683138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:07.854907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:09.140498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:10.572646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:12.020767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:13.286115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:14.607031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:16.076842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:23:59.857832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:01.263491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:02.632037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:04.038159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:05.229336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:06.774266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:07.944550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:09.270636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:10.665913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:12.125710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:13.375812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-12T18:24:14.716503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-12T18:24:20.427765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-12T18:24:20.597357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-12T18:24:20.768453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-12T18:24:20.931187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-12T18:24:21.102157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-12T18:24:16.264503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-12T18:24:16.499486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysq_invoicesq_itemsq_productsavg_ticketavg_recency_daysfrequencyq_returnsavg_basket_sizeavg_unique_basket_size
00178505391.2100372.000034.00001733.0000297.000018.152235.500017.000040.000050.97060.6176
11130473232.590056.00009.00001390.0000171.000018.904027.25000.028335.0000154.444411.6667
22125836705.38002.000015.00005028.0000232.000028.902523.18750.040350.0000335.20007.6000
3313748948.250095.00005.0000439.000028.000033.866192.66670.01790.000087.80004.8000
4415100876.0000333.00003.000080.00003.0000292.00008.60000.073222.000026.66670.3333
55152914623.300025.000014.00002102.0000102.000045.326523.20000.040129.0000150.14294.3571
66146885630.87007.000021.00003621.0000327.000017.219818.30000.0572399.0000172.42867.0476
77178095411.910016.000012.00002057.000061.000088.719835.70000.033541.0000171.41673.8333
881531160767.90000.000091.000038194.00002379.000025.54354.14440.2433474.0000419.71436.2308
99160982005.630087.00007.0000613.000067.000029.934847.66670.02440.000087.57144.8571

Last rows

df_indexcustomer_idgross_revenuerecency_daysq_invoicesq_itemsq_productsavg_ticketavg_recency_daysfrequencyq_returnsavg_basket_sizeavg_unique_basket_size
29585626177271060.250015.00001.0000645.000066.000016.06446.00001.00006.0000645.000066.0000
2959563617232421.52002.00002.0000203.000036.000011.708912.00000.15380.0000101.500015.0000
2960563717468137.000010.00002.0000116.00005.000027.40004.00000.40000.000058.00002.5000
2961564813596697.04005.00002.0000406.0000166.00004.19907.00000.25000.0000203.000066.5000
29625654148931237.85009.00002.0000799.000073.000016.95682.00000.66670.0000399.500036.0000
2963565812479473.200011.00001.0000382.000030.000015.77334.00001.000034.0000382.000030.0000
2964567914126706.13007.00003.0000508.000015.000047.07533.00000.750050.0000169.33334.6667
29655685135211092.39001.00003.0000733.0000435.00002.51124.50000.30000.0000244.3333104.0000
2966569515060301.84008.00004.0000262.0000120.00002.51531.00002.00000.000065.500020.0000
2967571412558269.96007.00001.0000196.000011.000024.54186.00001.0000196.0000196.000011.0000